In the field of data science and machine learning, the research team of Google is one of the leading contributors of many models, frameworks, data management systems, and many other utilities related to Machine Learning Operations(MLOPs). Vertex AI is another contribution from them which basically combines many other tools from Google under it. In this article, we are going to see why it can be helpful in most of the MLOPs applications. The major points to be covered in this article are listed below
Table of Contents
- Introduction to Vertex AI
- Workflow Proposed by Vertex AI for ML Development.
- Benefits of Vertex AI
- Features of VertexAI
- Components of MLOPs
- Tools for Machine Learning Operations in Vertex AI
- Tools to Interact with Vertex AI
Let’s start with having an introduction to Vertex AI.
Introduction to Vertex AI
Vertex AI is an API developed by Google research that consists of AutoML and AI Platform in one place. As we know the AutoML that allows us to train models on different kinds of data like image, video, text data, without writing much code and in AI Platform lets you run custom training code while training the model. Vertex AI provides options for both AutoML training and custom training. We can choose options for training and can easily save and deploy the models, and request vertex AI to predict the values according to the model which we have trained.
Workflow Proposed by Vertex AI for ML Development
You can use Vertex AI to manage the following stages in the ML workflow:
- Training flow.
- Create a dataset and upload data.
- Training the machine learning model on your data:
While training we can evaluate the accuracy of the model and if we are using custom training we can also use the hyperparameter tuning.
- Upload and store trained models on Vertex AI.
- Deploy the model to an endpoint so that we can serve predictions from the model.
- Send requests to our endpoint for making the predictions.
- Specify a prediction traffic split in your endpoint.
- Manage your models and endpoints.
Benefits of Vertex AI
Using Vertex AI has many benefits because it brings AutoML and AI Platforms together. Some of the benefits are
- Because it works with AutoMl we can make models with lower expertise in coding and also the amount of code we require to build the model is very low. We can also take the advantage of AutoML provided pre-trained APIs for computer vision and NLP.
- It supports advanced machine learning coding and also a low amount of codes to train the model with custom libraries.
- Its pipeline makes the machine learning models streamed and uses the features that make ML models serve their features to a user which reduces the complexity of self-service maintenance of the model.
Features of VertexAI
A unified UI and API for the entire Machine Learning workflow
Vertex AI consists of all the Google Cloud services for building Machine learning models under one unified UI and API that make us train the model and we can compare models using AutoML. For custom code training procedures, all our models can be stored in one repository. From where the chosen models can be deployed to the same endpoints of the Vertex AI.
Pre-trained APIs for vision, video, natural language, and more
In Vertex AI they have pre-trained APIs which can be easily used for vision, video, translation, and natural language ML and we can also build entirely new intelligent applications using a wide range of use cases of Vertex AI APIs. AutoML makes it easy for us to train high-quality models without having prior knowledge of the machine learning field which leads us on a requirement-oriented path.
End-to-end integration for data and AI
Since BigQuery supports the standard SQL queries we can use BigQuery to execute machine learning models on existing business intelligence tools. Vertex supports data labelling. Also using it we can generate high-quality labelled data and by exporting data from BigQuery we can push the data directly into Vertex AI for seamless End-to-end integration for data to AI.
Support for all open-source frameworks
Vertex AI supports a wide range of open-source frameworks such as TensorFlow, PyTorch, and scikit-learn which are famous in the field of machine learning, and also can be integrated with ML frameworks via custom containers which help in training and predicting the models.
Components of MLOPs
There can be many components of any machine learning operation. We can divide them roughly into four categories.
In ML operations one thing which is very much required is to train models in Vertex AI. We can train models using AutoML, or in any case of customization, we can use a wide range of customization options and use cases available in AI Platform Training. Using AI Platform Training we can take advantage of many different machine types to power your training jobs like distributed training, hyperparameter tuning, acceleration of processes with GPUs.
Deployment of machine learning models makes us serve the prediction of the model at an endpoint where the user can utilize the prediction. In vertex AI we can perform this also and with these they provide facilities for those models also which are not trained with vertex AI.
Labelled data performs well with the models and it becomes a primary requirement when we talk about the classification problem. Labelling data with higher accuracy is a high-priority task. Using the services of Vertex AI we can get the datasets annotated with higher accuracy by the human labourers. We just need to provide labelled samples to them.
When working with an organization we require to maintain transparency about all the machine learning operations we are performing in the development. In that scenario, we require a repository that can be shared with colleagues who are part of the project. Here Vertex AI provides the Feature store which is a fully managed repository that can easily flow across the organization. Also, it manages all the underlying infrastructure of the repository.
The above-given components of machine learning operations are basic and prioritized requirements for any development procedure which can be satisfied by the below-given tools of the Vertex AI.
Tools for Machine Learning Operations in Vertex AI
From the various tools of Vertex AI, the Following tools of Vertex AI can be used for the better and easier workflow of the development of machine learning applications.
- AutoML – It helps in the easy development of custom machine learning models with high-quality training routines.
- Deep learning VM images – Instantiate a VM image containing the most popular AI frameworks on a Compute Engine instance without worrying about software compatibility
- Vertex Matching Engine – It provides services for matching the similarities between the vectors.
- Vertex Data Labeling – For labelling the data better than the human labelling.
- Vertex Deep Learning Containers – Contains environments to deploy the AI applications.
- Vertex Edge Manager- Helps in monitor the edges and automation process of the APIs
- Vertex Explainable AI – Integrated into Vertex Prediction, AutoML Tables, and Notebooks to explain and understand the model predictions
- Vertex Model Monitoring – provides alerts for various concepts like data drift, concept drift which requires supervision for better performance of the model.
- Vertex Neural Architecture Search- Provides the new model architecture to satisfy the application of the models and optimizes models which already exist.
- Vertex Pipelines – Helps in streamlining the machine learning operations and also provides metadata tracking and continuous modelling. Built with TensorFlow EXtended and KuberFlow pipelines.
- Vertex Training – Provides various prebuilt algorithms and also allows the use of custom codes to train the models. We can train models either in a cloud environment or on offline premises.
- Vertex Vizier – Provide service for optimizing the hyperparameters.
Tools to interact with Vertex AI
Google Cloud Console
We can deploy our models on the cloud Google Cloud Console. They provide the facility to manage datasets, models, endpoints, and jobs. This option also gives a user interface for working with machine learning resources.
Cloud client libraries
Vertex AI provides client libraries for some languages which can help in calling the Vertex AI API. developers can get optimized experience by using each supported language in conventional ways. Information about the supported languages is given here.
Google API Client Libraries can be used as an alternative for calling the Vertex AI API in cases where we want to use other languages. When using the Google API Client Libraries, this is an easier and lower code process than working with HTTP requests.
The Vertex AI REST API provides RESTful APIs for managing jobs, models, and endpoints, and for making predictions with hosted models on Google Cloud.
Deep Learning VM Images
Deep Learning VM Images consists of various virtual machine images for optimizing many data science and machine learning tasks. All images have pre-installed key ML frameworks which can be used out of the box on instances with GPUs also which cause the acceleration of the data processing tasks. There are currently images which are supporting TensorFlow Enterprise, TensorFlow, PyTorch, and generic high-performance computing, with every version
Deep Learning Containers
Deep Learning Containers consist of various Docker containers in which we have various pre-installed key data science frameworks, libraries, and tools. These containers contain consistent environments which are pre-optimized for higher performance, which helps in prototype and implement workflows quickly.
Here in the article, we have seen various information about the vertex AI which can help us in improving our performance of the machine learning operations. We have seen how they become more useful by combining major products powered by Google’s leading AI research. A reader can check the pricing for different activities using Vertex AI here.
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Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.